Systematic Assessment of a High Impact Course Design Institute

Herein, we describe an intensive, week long course design institute (CDI) designed to introduce participants to the scholarly and evidence driven process of learning focused course design. Impact of this intervention is demonstrated using a multifaceted approach: (a) post CDI satisfaction and perception surveys, (b) pre /post CDI surveys probing pedagogical confidence and perceptions regarding importance of syllabi components, and (c) pre /post CDI syllabi analysis using a reliable syllabus rubric validated for higher education courses. The combined results of these qualitative and quantitative studies indicate that participants value the CDI experience, believe they learn basic principles of learning focused course design, report they are more confident enacting learning focused concepts in the classroom, believe they are better able to design learning focused syllabi, and they actually design more learning focused courses/syllabi

Fingerloop activates cargo delivery and unloading during cotranslational protein targeting

During cotranslational protein targeting by the signal recognition particle (SRP), information about signal sequence binding in the SRP's M domain must be effectively communicated to its GTPase domain to turn on its interaction with the SRP receptor (SR) and thus deliver the cargo proteins to the membrane. A universally conserved “fingerloop” lines the signal sequence–binding groove of SRP; the precise role of this fingerloop in protein targeting has remained elusive. In this study, we show that the fingerloop plays important roles in SRP function by helping to induce the SRP into a more active conformation that facilitates multiple steps in the pathway, including efficient recruitment of SR, GTPase activation in the SRP•SR complex, and most significantly, the unloading of cargo onto the target membrane. On the basis of these results and recent structural work, we propose that the fingerloop is the first structural element to detect signal sequence binding; this information is relayed to the linker connecting the SRP's M and G domains and thus activates the SRP and SR for carrying out downstream steps in the pathway

Genome sequences of three hpAfrica2 strains of Helicobacter pylori

We present the genome sequences of three hpAfrica2 strains of Helicobacter pylori, which are postulated to have evolved in isolation for many millennia in people of San ethnicity. Although previously considered to be ancestral to Helicobacter acinonychis, the hpAfrica2 strains differ markedly from H. acinonychis in their gene arrangement. These data provide new insights into Helicobacter evolution

Australian health care providers' views on opt-out HIV testing

Background: Opt-out HIV testing is a novel concept in Australia. In the opt-out approach, health care providers (HCPs) routinely test patients for HIV unless they explicitly decline or defer. Opt-out HIV testing is only performed with the patients' consent, but pre-test counselling is abbreviated. Australian national testing guidelines do not currently recommend opt-out HIV testing for the general population. Non-traditional approaches to HIV testing (such as opt-out) could identify HIV infections and facilitate earlier treatment, which is particularly important now that HIV is a chronic, manageable disease. Our aim was to explore HCPs' attitudes toward opt-out HIV testing in an Australian context, to further understanding of its acceptability and feasibility. Methods: In this qualitative study, we used purposeful sampling to recruit HCPs who were likely to have experience with HIV testing in Western Australia. We interviewed them using a semi-structured guide and used content analysis as per Graneheim to code the data. Codes were then merged into subcategories and finally themes that unified the underlying concepts. We refined these themes through discussion among the research team. Results: Twenty four HCPs participated. Eleven participants had a questioning attitude toward opt-out HIV testing, while eleven favoured the approach. The remaining two participants had more nuanced perspectives that incorporated some characteristics of the questioning and favouring attitudes. Participants' views about opt-out HIV testing largely fell into two contrasting themes: normalisation and routinisation versus exceptionalism; and a need for proof versus openness to new approaches. Conclusion: Most HCPs in this study had dichotomous attitudes toward opt-out HIV testing, reflecting contrasting analytical styles. While some HCPs viewed it favourably, with the perceived benefits outweighing the perceived costs, others preferred to have evidence of efficacy and cost-effectiveness

Evaluating model outputs using integrated global speleothem records of climate change since the last glacial

Although quantitative isotopic data from speleothems has been used to evaluate isotope-enabled model simulations, currently no consensus exists regarding the most appropriate methodology through which to achieve this. A number of modelling groups will be running isotope-enabled palaeoclimate simulations in the framework of the Coupled Model Intercomparison Project Phase 6, so it is timely to evaluate different approaches to use the speleothem data for data-model comparisons. Here, we illustrate this using 456 globally-distributed speleothem δ18O records from an updated version of the Speleothem Isotopes Synthesis and Analysis (SISAL) database and palaeoclimate simulations generated using the ECHAM5-wiso isotope-enabled atmospheric circulation model. We show that the SISAL records reproduce the first-order spatial patterns of isotopic variability in the modern day, strongly supporting the application of this dataset for evaluating model-derived isotope variability into the past. However, the discontinuous nature of many speleothem records complicates procuring large numbers of records if data-model comparisons are made using the traditional approach of comparing anomalies between a control period and a given palaeoclimate experiment. To circumvent this issue, we illustrate techniques through which the absolute isotopic values during any time period could be used for model evaluation. Specifically, we show that speleothem isotope records allow an assessment of a model’s ability to simulate spatial isotopic trends. Our analyses provide a protocol for using speleothem isotopic data for model evaluation, including screening the observations to take into account the impact of speleothem mineralogy on 18O values, the optimum period for the modern observational baseline, and the selection of an appropriate time-window for creating means of the isotope data for palaeo time slices

A View from the Past Into our Collective Future: The Oncofertility Consortium Vision Statement

Today, male and female adult and pediatric cancer patients, individuals transitioning between gender identities, and other individuals facing health extending but fertility limiting treatments can look forward to a fertile future. This is, in part, due to the work of members associated with the Oncofertility Consortium. The Oncofertility Consortium is an international, interdisciplinary initiative originally designed to explore the urgent unmet need associated with the reproductive future of cancer survivors. As the strategies for fertility management were invented, developed or applied, the individuals for who the program offered hope, similarly expanded. As a community of practice, Consortium participants share information in an open and rapid manner to addresses the complex health care and quality-of-life issues of cancer, transgender and other patients. To ensure that the organization remains contemporary to the needs of the community, the field designed a fully inclusive mechanism for strategic planning and here present the findings of this process. This interprofessional network of medical specialists, scientists, and scholars in the law, medical ethics, religious studies and other disciplines associated with human interventions, explore the relationships between health, disease, survivorship, treatment, gender and reproductive longevity. The goals are to continually integrate the best science in the service of the needs of patients and build a community of care that is ready for the challenges of the field in the future

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI